{"id":"W6889010978","doi":"10.25345/c5w37m65t","title":"MassIVE MSV000094631 - GNPS.huzhang UHPLC networking UCSD rhr","year":2024,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Yorkville University","funders":"","keywords":"Identification (biology); Process (computing); Set (abstract data type); Troubleshooting; Selection (genetic algorithm)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.000810657,0.001725096,0.00157409,0.001193318,0.0003864557,0.000822774,0.002155231,0.001329847,0.01108956],"category_scores_gemma":[0.0002444364,0.00172774,0.0007525483,0.001748686,0.0003567962,0.0002938712,0.001632601,0.00286517,0.3453464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009458461,"about_ca_system_score_gemma":0.0004170375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003942508,"about_ca_topic_score_gemma":0.003254916,"domain_scores_codex":[0.9925128,0.0004060471,0.001177066,0.002238204,0.001549506,0.00211642],"domain_scores_gemma":[0.995059,0.0004475639,0.0007750016,0.002962617,0.0001720019,0.0005837911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006666685,0.0001313947,0.00003946239,0.000534108,0.0007023728,0.003220091,0.00007493926,0.00004468308,0.00004251439,0.00005257127,0.9936684,0.00142279],"study_design_scores_gemma":[0.000480544,0.0001086786,0.00006314196,0.001701052,0.001411189,0.0001044336,0.0001079626,0.0001384256,0.00003539843,0.0005647379,0.9934485,0.001835935],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004764814,0.01089909,0.000004009307,0.00007034562,0.009014145,0.001199209,0.9705272,0.0009865799,0.007251753],"genre_scores_gemma":[0.0002208911,0.0004667555,0.0001249634,0.0005748449,0.01009801,0.0004393061,0.9851432,0.0008011878,0.002130839],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3342568,"threshold_uncertainty_score":0.9999666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397527430396278,"score_gpt":0.2858478824084768,"score_spread":0.261872608104514,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}